Adapted from Stackoverflow examples
library(plotly)
## Loading required package: ggplot2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(reshape2)
data(iris)
attach(iris)
Generate plot on three quantitative variables
iris_plot <- plot_ly(iris,
x = ~Sepal.Length,
y = ~Sepal.Width,
z = ~Petal.Length,
type = "scatter3d",
mode = "markers",
size = 0.02)
iris_plot
Load Data (Regression object)
petal_lm <- lm(Petal.Length ~ 0 + Sepal.Length + Sepal.Width,data = iris)
Setting resolution parameter
graph_reso <- 0.05
Setup Axis
axis_x <- seq(min(iris$Sepal.Length), max(iris$Sepal.Length), by = graph_reso)
axis_y <- seq(min(iris$Sepal.Width), max(iris$Sepal.Width), by = graph_reso)
Regression surface
Rearranging data for plotting
petal_lm_surface <- expand.grid(Sepal.Length = axis_x,Sepal.Width = axis_y,KEEP.OUT.ATTRS = F)
petal_lm_surface$Petal.Length <- predict.lm(petal_lm, newdata = petal_lm_surface)
petal_lm_surface <- acast(petal_lm_surface, Sepal.Width ~ Sepal.Length, value.var = "Petal.Length")
hcolors=c("orange","blue","green")[iris$Species]
iris_plot <- plot_ly(iris,
x = ~Sepal.Length,
y = ~Sepal.Width,
z = ~Petal.Length,
text = "Species",
type = "scatter3d",
color = ~Species,
colors = c("orange","blue","green"),
mode = "markers",
size=0.02)
Add surface
iris_plot <- add_trace(p = iris_plot,
z = petal_lm_surface,
x = axis_x,
y = axis_y,
inherit = F,
type = "surface")
iris_plot